Application Service Providers (ASPs) obtaining resources from multiple clouds have to contend with different management and control platforms employed by the cloud service providers (CSPs) and network service providers (NSP). Distributing applications on multiple clouds has a number of benefits but absence of a common multi-cloud management platform that would allow ASPs dynamic and real time control over resources across multiple clouds and interconnecting networks makes this task arduous. OpenADN, being developed at Washington University in Saint Louis, fills this gap. However, performance issues of such a complex, distributed and multi-threaded platform, not tackled appropriately, may neutralize some of the gains accruable to the ASPs. In this paper we establish the need for and methods of collecting precise and fine-grained behavioral data of OpenADN like platforms that can be used to optimize their behavior in order to control operational cost, performance (e.g., latency) and energy consumption.